Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number o...

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Vydané v:	IEEE transactions on signal processing Ročník 70; s. 1625 - 1640
Hlavní autori:	Wang, Shuai, Chang, Tsung-Hui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Clustering Clustering algorithms Communication Convergence Cost analysis Data models Distributed databases Factorization Federated learning gradient sharing Machine learning matrix factorization model averaging Partitioning algorithms Servers Signal processing Signal processing algorithms
ISSN:	1053-587X, 1941-0476
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